public ArrayList<String> getPosteriorDecodingPath(
SingleDPMatrix fMatrix, SingleDPMatrix bMatrix) {
ArrayList<String> pdPath = new ArrayList<String>();
State states[] = fMatrix.states();
double fScore = fMatrix.getScore();
double fMatrixScores[][] = fMatrix.scores;
double bMatrixScores[][] = bMatrix.scores;
int numberOfObservations = fMatrixScores.length;
int numberOfStates = states.length;
for (int i = 1; i < numberOfObservations - 1; i++) {
ArrayList<Double> probOfStates = new ArrayList<Double>();
for (int s = 0; s < numberOfStates; s++) {
double probOfOneState = Math.exp(fMatrixScores[i][s] + bMatrixScores[i][s] - fScore);
probOfStates.add(probOfOneState);
}
Object max_obj = Collections.max(probOfStates);
int maxValueIndex = probOfStates.indexOf(max_obj);
State topScoredState = states[maxValueIndex];
pdPath.add(topScoredState.getName());
// System.out.print(topScoredState.getName());
}
// System.out.println();
// System.out.println("\n number of rows is " + numberOfObservations);
// System.out.println("\n length of posterior decoding path is " + pdPath.size());
return pdPath;
} /*getPosteriorDecodingPath*/
public ArrayList<Double> getPosteriorDecodingScores(
SingleDPMatrix fMatrix, SingleDPMatrix bMatrix) {
ArrayList<Double> pdScores = new ArrayList<Double>();
State states[] = fMatrix.states();
double fScore = fMatrix.getScore();
double fMatrixScores[][] = fMatrix.scores;
double bMatrixScores[][] = bMatrix.scores;
int numberOfObservations = fMatrixScores.length;
int numberOfStates = states.length;
for (int i = 1; i < numberOfObservations - 1; i++) {
double sum = 0;
double g = Double.MIN_VALUE;
for (int s = 0; s < numberOfStates; s++) {
g = getWeight(states[s].getName());
/*
if( states[s].getName().equals("M")){
g=0;
}
else
{
g=1;
}
*/
double probOfOneState = Math.exp(fMatrixScores[i][s] + bMatrixScores[i][s] - fScore);
sum = sum + (probOfOneState * g);
}
pdScores.add(sum);
}
return pdScores;
} /*getPosteriorDecodingScores*/
public static void main(String[] args) throws Exception {
// TODO Auto-generated method stub
// TODO Auto-generated method stub
VITERBI3 app = new VITERBI3();
String serDirName = app.getSerDir();
String aliDirName = app.getAliDir();
String[] stateNames = app.getStates();
double[] strProb = app.getStartProbabilities();
double[][] tranProb = app.getTransitionProbability();
app.checkTransitionMatrixNormality(tranProb);
double scoreThr = app.getScoreThreshold();
int minLenForTrhoughs = app.getMinLengthThreshold();
int minLenForPeakDis = app.getMinPeakLengths();
int lengthThresholdForBlocks = app.getBlockLengthThreshold();
double backGroundAlpha[] = {
0.7, 0.8, 1
}; // {0.15106438458196936,0.20973711740901407,0.22900628496706044} {0.8, 0.9, 1}
double greenStateAlpha[] = {
0.9, 2.3, 1
}; // {0.1713002602078479,11.729438447100865,1.3763120488468623}; {0.9,2.3,1}
double greenFollowingMixedAlpha[] = {
0.8, 2.3, 0.9
}; // {0.16327302239493097,9.219505194760753,1.8283941303615243} {0.8,2.3,0.9}
double greenToRedEdgeAlpah[] = {
1.3, 1.4, 1.2
}; // {0.4799631840073914,0.4944929532552769,0.2857067163030646}; {1.3,1.4,1.2}
double redStateAlpha[] = {
2.5, 1, 0.8
}; // {71.25302335371902,0.201959003605188,0.20299272598532336} {5,1.1,0.9}
double redToGreenEdgeAlpha[] = {
5, 1.1, 0.9
}; // {0.39728078185044136,0.3710506533397517,0.2490053255191496}{1.4,1.3,1.2}
// double redFollowingMixedAlpha [] = {10,0.1,0.1};
// //{4.5200496933818615,0.1772646945273629,0.17979061871698085};
double junctionStateAlpha[] = {0.7, 0.8, 1};
LinkedHashMap<String, double[]> statesAndDirPar = new LinkedHashMap<String, double[]>();
// statesAndDirPar.put("P", x);
statesAndDirPar.put("M", backGroundAlpha);
statesAndDirPar.put("r", redStateAlpha);
statesAndDirPar.put("E", redToGreenEdgeAlpha);
statesAndDirPar.put("G", greenStateAlpha);
statesAndDirPar.put("g", greenFollowingMixedAlpha);
statesAndDirPar.put("e", greenToRedEdgeAlpah);
statesAndDirPar.put("R", redStateAlpha);
statesAndDirPar.put("J", junctionStateAlpha);
File serDir = new File(serDirName);
for (File aSerFile : serDir.listFiles()) {
String oneFileName = aSerFile.getName();
if (oneFileName.startsWith(".")) {
continue;
}
BLOCK block = new BLOCK(aSerFile, aliDirName);
String blockId = block.blockId;
int blockLength = app.getBlockLength(blockId);
if (blockLength < lengthThresholdForBlocks) {
// System.out.println(" found sequence :" + blockId + " with too short length :" +
// blockLength);
continue;
}
Matrix2D m = block.blockMatrix;
Sequence seq = block.blockSeq;
ArrayList<Integer> rowsWithSumZero = block.missingData;
SimpleAlphabet observedSeqAlphabet = block.blockObservedSeqAlphabet;
SimpleSymbolList symbolList = block.blockSimpleSymbolList;
// System.out.println("seq " + blockId + " is processed");
MarkovModel mm =
BLOCK.makeMarkovModel(
observedSeqAlphabet, tranProb, strProb, statesAndDirPar, "dirichletMM");
DP dp = new SingleDP(mm);
SymbolList[] symList = {symbolList};
StatePath viterbiPath = dp.viterbi(symList, ScoreType.PROBABILITY);
SymbolList symbolsInViterbi = viterbiPath.symbolListForLabel(StatePath.STATES);
ArrayList<String> viterbiPathAsAnArrayList = app.symbolListToArrayList(symbolsInViterbi);
System.out.println("veterbi path length for " + blockId + " is " + viterbiPath.length());
ArrayList<String> ViterbiPath = new ArrayList<String>();
for (int i = 1; i <= symbolsInViterbi.length(); i++) {
Symbol oneSym = symbolsInViterbi.symbolAt(i);
ViterbiPath.add(oneSym.getName());
// System.out.print(oneSym.getName());
}
System.out.println();
boolean isFiltering = app.getFilterMeaningLessPicks();
app.filterMeaningLessPicks(ViterbiPath, isFiltering);
ArrayList<Double> viterbiTranslatedToNumbersAndMappedToAlingment =
app.mapViterbiPathToAlignment(ViterbiPath, seq, rowsWithSumZero);
String outputFileName =
"/Users/hk3/Desktop/Main/Composure_Droshophila_Model/ANALYSIS_EISENLAB/2L/ViterbiPaths/VITERBI2_"
+ blockId
+ ".txt";
app.printoutViterbiPath(viterbiTranslatedToNumbersAndMappedToAlingment, outputFileName);
SingleDPMatrix forwardMatrix =
(SingleDPMatrix) dp.forwardMatrix(new SymbolList[] {symbolList}, ScoreType.PROBABILITY);
double score = forwardMatrix.getScore();
// System.err.printf("Forward: %g%n", score);
System.out.println();
SingleDPMatrix backwardMatrix =
(SingleDPMatrix) dp.backwardMatrix(new SymbolList[] {symbolList}, ScoreType.PROBABILITY);
ArrayList<Double> posteriorScores =
app.getPosteriorDecodingScores(forwardMatrix, backwardMatrix);
// posteriorScores = app.fillInShortTroughs(posteriorScores, scoreThr, minLenForTrhoughs);
// posteriorScores = app.filterOutShortPeaks(posteriorScores, scoreThr, minLenForPeakDis);
// posteriorScores = app.getEnhancerRegions(posteriorScores, scoreThr);
ArrayList<Double> PDScoresMapedToalignment =
app.mapPDScoresToAlignment(posteriorScores, seq, rowsWithSumZero);
String posteriorOutputFileName =
"/Users/hk3/Desktop/Main/Composure_Droshophila_Model/ANALYSIS_EISENLAB/2L/ViterbiPaths/Posterior_"
+ blockId
+ ".txt";
app.printoutViterbiPath(PDScoresMapedToalignment, posteriorOutputFileName);
// writte chains into gff files
String chainDir = app.getChainDir();
app.wirteChainsIntoAGffFile(PDScoresMapedToalignment, blockId, chainDir, scoreThr);
}
} /*main*/
